Learning to Recommend with Hidden Factor Models and Social Trust Ensemble

DOI

10.2991/csic-15.2015.20How to use a DOI?

Keywords

Recommender systems, Latent factor model, Latent dirichlet allocation, Social network, Social trust ensemble

Abstract

As one of the most successful approaches to building recommender systems, CollaborativeFiltering (CF) uses the known preferences of a group of users to make recomm-endations or predictions of the unknown preferences for other users. Themost successful approaches to CF are latent factor models, Latent Dirichlet Allocation (LDA) models, which directly profile both users and products, and trust-based collaborative filtering models, which analyze the connections among users. This paper introduces some innovations to both approaches. The factor, topic andtrust models can now be smoothly merged, to build a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods aretested on the Yelp data, and the results are better than those previously published on that dataset.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dan Zhao
AU  - Junyi Wang
AU  - Andi Gao
AU  - Pengfei Yue
PY  - 2015/07
DA  - 2015/07
TI  - Learning to Recommend with Hidden Factor Models and Social Trust Ensemble
BT  - Proceedings of the 2015 International Conference on Computer Science and Intelligent Communication
PB  - Atlantis Press
SP  - 87
EP  - 91
SN  - 2352-538X
UR  - https://doi.org/10.2991/csic-15.2015.20
DO  - 10.2991/csic-15.2015.20
ID  - Zhao2015/07
ER  -